Predictive analytics has become increasingly popular in the world of big data. Predictive analytics is the application of statistical algorithms on a dataset to identify patterns for predicting future events. These identified patterns can be exploited to identify potential risks and opportunities. For example, forecasting analysis can predict future performance based on collected data. As another example, probability analysis can predict the likelihood that a scenario will occur. Business organizations that can accurate predict future events will be more successful since they can adjust their business to best exploit the patterns found in the data.
Although predictive analytics is a valuable tool, many business organizations have difficult applying it. First of all, there are many different types of statistical algorithms and the applicability of each can be situational depending on the collected data. Secondly, the behavior of each statistical algorithm can vary depending on the input parameters applied to the statistical algorithm. As a result, a user requires a deep understanding of the different types of statistical algorithms available and the scenarios where each algorithm can be applied to accurately forecast future events. Given that most users in the business organization lack this deep understanding, they are unable to take advantage of predictive analytics.